Instructions to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Ollama
How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with Ollama:
```
ollama run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M
```

Lemonade

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.QAZTECH-Azure-Engineer-8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "qaztech-platform/QAZTECH-Azure-Engineer-8B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

QAZTECH® Azure Engineer 8B — GGUF

Quantized builds of QAZTECH Azure Engineer 8B for llama.cpp, Ollama and LM Studio: QAZTECH-Azure-Engineer-8B-Q4_K_M.gguf (5.0 GB) and QAZTECH-Azure-Engineer-8B-Q8_0.gguf (8.7 GB). The evaluation below was run on Q4_K_M.

Open multilingual assistant for Azure engineers. It writes, fixes, explains and converts infrastructure code, migrates ARM templates to Bicep or Terraform, reviews deployments for cost (FinOps tags Environment, CostCenter, Owner; lower-cost dev SKUs) and troubleshoots failed deployments from their error logs. Prose in English, Russian, Kazakh, Arabic and Spanish; code, commands and comments in English.

Tools: Azure CLI, Azure PowerShell, Bicep, ARM JSON, Terraform (azurerm, azapi), Azure Policy and custom RBAC roles, KQL, Azure Pipelines and GitHub Actions YAML.

Try it

ollama run qaztech-platform/azure-engineer

Evaluation

200 held-out examples (not used in training), same system prompt, temperature 0.3. Code blocks are checked with real tools: Azure CLI 2.89.1 (--help for every command and flag), Az PowerShell modules (cmdlets and parameters), Bicep CLI 0.48.1 (bicep build), Terraform 1.16.6 with azurerm 5.10.0 and azapi 2.13.0 (terraform validate).

Model	Answer in the right language	Reasoning-trace leaks	Answers with all code valid	Code blocks with errors
QAZTECH Azure Engineer 8B	99%	0	76%	21%
Qwen3-8B (base, same prompt)	98%	0	65%	22%

The held-out set comes from the same distribution as the training data, so these numbers measure in-distribution quality. Tool checks verify that commands, flags, parameters, resource types and properties exist; they do not verify values (for example VM size names).

Training

Base: Qwen/Qwen3-8B (Apache-2.0). LoRA r=16, alpha=32, dropout 0.05, all linear layers; 3 epochs, lr 1e-4 cosine, effective batch 32, max length 3,072; 1x A100 40 GB; merged into bf16 weights.
Held-out eval loss 0.461, token accuracy 0.865.
Data: 4,716 synthetic examples (4,516 train, 200 held out) generated from code samples in Microsoft Learn documentation (CC BY 4.0) by teacher models Qwen3-30B-A3B-Instruct-2507 (4,118 examples, run locally) and MiniMax M3.1 Flash via Ollama Cloud (598 examples). Terraform prompts included the authoritative azurerm 5.10 resource schema.
Every code block was checked with the tools listed above; failing examples were repaired with tool-grounded hints (913) or dropped. Language match, leak and duplicate filters applied.
Modes: troubleshoot 951, finops 868, fix 824, write 726, explain 717, convert 353, migrate 277. Languages: ar 996, ru 995, es 987, en 939, kk 799.

Limitations

Facts can be wrong: invented flags or parameters and wrong values (for example B2s instead of Standard_B2s) were observed. Verify every command against Microsoft Learn and run what-if / terraform plan before applying.
Terraform (54 examples) and Bicep (226) are less represented than Azure CLI, PowerShell and ARM.
Kazakh and Arabic output has not been reviewed by native speakers.
Not affiliated with or endorsed by Microsoft or HashiCorp.

Licence and attribution

Weights: Apache-2.0. Base model: Qwen3-8B by Alibaba Cloud (Apache-2.0). Teacher models: Qwen3-30B-A3B-Instruct-2507 (Apache-2.0) and MiniMax M3.1 Flash (accessed through Ollama Cloud). Source material: Microsoft Learn documentation, CC BY 4.0. Microsoft and Azure are trademarks of the Microsoft group of companies; Terraform is a trademark of HashiCorp.

Trademark

QAZTECH is a registered trade mark of AISC Technologies LTD in the United Kingdom (UK IPO nos. UK00004263700, UK00004317681, UK00004373195). The Apache-2.0 licence does not grant rights to use the QAZTECH name or logo (section 6). Derivative models must not use "QAZTECH" in their name; describe them as "based on QAZTECH Azure Engineer".